{"uid":"cap_mrKqbMH3dbOKCFyOjwdJh","slug":"citiview-document-chunker-with-embeddings-e8650d15","name":"Citiview Document Chunker with Embeddings","description":"Ready-to-search chunks: turns a URL (page, PDF, Office file) or text into clean overlapping chunks with character offsets and bge-m3 embeddings (1024 dims) in one call, ready for any vector store. Up to 300 chunks. Also callable as GET with the same fields as query parameters.","url":"https://agents.citiviewgroup.com/v1/chunk?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","properties":{"url":{"type":"string"},"text":{"type":"string"},"embed":{"type":"boolean","description":"Include embeddings (default true)"},"overlap":{"type":"integer","description":"Default 150"},"chunk_size":{"type":"integer","description":"Characters per chunk, 200 to 4000 (default 1000)"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.015","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.015/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.015","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.015","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_agjwPh9suv2Bprp2zdsR2","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.015","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Converts a URL (web page, PDF, Office file) or raw text into clean overlapping chunks with character offsets and bge-m3 embeddings (1024 dims), ready for vector store ingestion.","exampleAgentPrompt":"Chunk this PDF at https://example.com/report.pdf into overlapping segments of 1000 characters each with 150-character overlap, and include bge-m3 embeddings so I can load them straight into my vector store.","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"Take this research paper at https://arxiv.org/pdf/2310.01513.pdf, split it into 800-character chunks with 200-character overlap, and give me the bge-m3 embeddings so I can push them into my Pinecone index."},{"title":"Semantic search over company policy","prompt":"Chunk our employee handbook at https://acme.com/handbook.pdf into 1000-character pieces with 150-character overlap and embed them with bge-m3 — I want to build a search tool over it."},{"title":"Ad-hoc text embedding for a knowledge base","prompt":"I have a long article I'll paste as text — split it into overlapping 500-character chunks with 100-character overlap and return the embeddings so I can add it to our knowledge base."}],"resultDescription":"Returns up to 300 text chunks, each with its text content, start and end character offsets within the original document, and a 1024-dimensional bge-m3 embedding vector. The embeddings are immediately usable with any vector store (Pinecone, Weaviate, Qdrant, etc.) for semantic similarity search.","failureModes":["URL is unreachable or returns a non-200 response — chunking fails with an error","Unsupported file format at the provided URL — only web pages, PDFs, and Office files are supported","chunk_size out of range (must be 200–4000) — request rejected with validation error","Document exceeds the 300-chunk limit — output is capped at 300 chunks","embed=false requested — embeddings are omitted from the response","Oversized document may time out or be truncated before 300 chunks are produced"],"whenToPreferThis":"Choose this endpoint when you need both chunking and embedding in a single call, specifically with bge-m3 (1024-dim) embeddings suitable for multilingual semantic search. It is ideal for RAG pipeline ingestion where you want overlapping chunks with character offsets from a URL or raw text, without managing a separate embedding model. Prefer alternatives if you need a different embedding model, more than 300 chunks, or only chunking without embeddings.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-14T06:46:39.484Z","isFirstParty":false,"canonicalSlug":"citiview-document-chunker-with-embeddings-e8650d15"}